No more language barriers: AI translation is ready for prime time

Translation is now a viable system feature

The new wave of generative AI has made language translation a practical and scalable feature within our existing systems. This is particularly relevant in industries like in-home care, where many staff work in English as a second, third, or even seventh language.

It’s now entirely feasible to let workers take notes in their native language, have those notes translated into English, and even summarised or made searchable via an assistant-style AI chat interface. We can also retain the original-language version for cross-checking or reinterpretation if needed.

Conversely, English notes already in the system can be translated on the fly into a worker’s first language, reducing the risk of misunderstanding and increasing confidence that everyone is on the same page.

Why this is now viable

Three major developments have made this possible:

1. Translation quality has radically improved.

Compared to earlier tools like Google Translate, today’s generative AI models offer a huge leap in real-world language understanding and output. These models are built on "large language models," meaning language itself is at the core of their design. We're no longer experimenting with rough approximations – we’re getting highly usable results, right out of the box.

2. Language services are already available from existing vendors.

Many of our current vendors now offer these language capabilities as ready-to-use services. There’s no need for upfront infrastructure investment or long planning phases. We can begin directly with the work of assembling lightweight, targeted solutions. That alone removes a major barrier to entry – what would have been a multimillion-dollar integration project a few years ago can now start with a few API calls.

3. AI speeds up integration.

As outlined in "From planning to rollout: AI speeds up every step of a project" generative AI accelerates the one-off job of integrating these services into our systems, including establishing the necessary security guardrails. The speed and ease of this step further lower the cost and risk profile.

Pilot first, integrate later

We no longer need to commit to a full system rollout up front. Pilots with one or two users are now fast and cost-effective. We can launch a standalone prototype, test how it performs under real-world conditions, and then integrate it with our core systems only once it proves its value.

This shift in approach is critical. It means we can explore and implement impactful translation tools without committing to legacy-scale delivery models. The tech is ready, the barriers are down, and the benefits are within reach.

Andrew Walker 
10 years AI strategy & implementation for executives in staff-heavy organisations, often with mobile workforces.

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